Supervised Data in Backpropagation and Genetics Learning Algorithms

The purpose of this research project was to address the efficiency of the Backpropagation and Genetics learning algorithms when dealing with supervised data. The efficiency of a learning algorithm was determined by both the speed at which the learning algorithm was able to train a Neural Network along with its overall accuracy at the given task.

Research Tests

Test 1: XOR Gate

The goal of this test was to address the efficency of each learning algorithm when it comes to simple tasks such as the XOR Gate. The parameters for each learning algorithm are as follows:

Genetics:

• Mutation rate: 0.05
• Population size: 500

Backpropagation:

• Learning rate: 0.2
• Momentum rate: 0.0125

	Backpropagation Training Time	Genetics Training Time
Minimum:	163.9362	169.9704
1st Quartile:	239.348025	214.5134
3rd Quartile:	406.024125	335.4027
Maximum:	647.6394	1863.1769

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Test 2: Reptile Classification

The goal of this test was to address the accuracy of each learning algorithm given a limited amount of time. (ADD MORE HERE) The parameters for each learning algorithm are as follows:

Genetics:

• Mutation rate: 0.05
• Population size: 125

Backpropagation:

• Learning rate: 0.035
• Momentum rate: 0.0125

Acknowledgements

A huge thank you to Great Minds Robotics and Ryan Scopio for their advice/guidance throughout the research project.